diff --git a/pygrinder/block_missing/block_missing.py b/pygrinder/block_missing/block_missing.py
index a977c94..4a26c13 100644
--- a/pygrinder/block_missing/block_missing.py
+++ b/pygrinder/block_missing/block_missing.py
@@ -16,7 +16,7 @@ def random_select_start_indices(
     block_width,
     feature_idx,
     step_idx,
-    p,
+    hit_rate,
     n_samples,
     n_steps,
     n_features,
@@ -27,7 +27,7 @@ def random_select_start_indices(
     all_feature_start_indices = [i * n_steps for i in all_feature_indices]
     selected_feature_start_indices = np.random.choice(
         all_feature_start_indices,
-        math.ceil(len(all_feature_start_indices) * p),
+        math.ceil(len(all_feature_start_indices) * hit_rate),
         replace=False,
     )
     selected_feature_start_indices = np.asarray(selected_feature_start_indices)
@@ -57,8 +57,9 @@ def _block_missing_numpy(
     X = np.copy(X)
 
     n_samples, n_steps, n_features = X.shape
+    hit_rate = p * n_steps * n_features / (block_len * block_width)
     start_indices = random_select_start_indices(
-        block_width, feature_idx, step_idx, p, n_samples, n_steps, n_features
+        block_width, feature_idx, step_idx, hit_rate, n_samples, n_steps, n_features
     )
 
     X = X.transpose(0, 2, 1)
@@ -83,8 +84,9 @@ def _block_missing_torch(
     X = torch.clone(X)
 
     n_samples, n_steps, n_features = X.shape
+    hit_rate = p * n_steps * n_features / (block_len * block_width)
     start_indices = random_select_start_indices(
-        block_width, feature_idx, step_idx, p, n_samples, n_steps, n_features
+        block_width, feature_idx, step_idx, hit_rate, n_samples, n_steps, n_features
     )
 
     X = X.transpose(1, 2)
diff --git a/pygrinder/sequential_missing/seq_missing.py b/pygrinder/sequential_missing/seq_missing.py
index d084133..2f66e5d 100644
--- a/pygrinder/sequential_missing/seq_missing.py
+++ b/pygrinder/sequential_missing/seq_missing.py
@@ -15,7 +15,7 @@
 def random_select_start_indices(
     feature_idx,
     step_idx,
-    p,
+    hit_rate,
     n_samples,
     n_steps,
     n_features,
@@ -31,7 +31,7 @@ def random_select_start_indices(
 
     selected_feature_start_indices = np.random.choice(
         all_feature_start_indices,
-        math.ceil(len(all_feature_start_indices) * p),
+        math.ceil(len(all_feature_start_indices) * hit_rate),
         replace=False,
     )
     selected_feature_start_indices = np.asarray(selected_feature_start_indices)
@@ -57,8 +57,9 @@ def _seq_missing_numpy(
     X = np.copy(X)
 
     n_samples, n_steps, n_features = X.shape
+    hit_rate = p * n_steps / seq_len
     start_indices = random_select_start_indices(
-        feature_idx, step_idx, p, n_samples, n_steps, n_features
+        feature_idx, step_idx, hit_rate, n_samples, n_steps, n_features
     )
 
     X = X.transpose(0, 2, 1)
@@ -82,8 +83,9 @@ def _seq_missing_torch(
     X = torch.clone(X)
 
     n_samples, n_steps, n_features = X.shape
+    hit_rate = p * n_steps / seq_len
     start_indices = random_select_start_indices(
-        feature_idx, step_idx, p, n_samples, n_steps, n_features
+        feature_idx, step_idx, hit_rate, n_samples, n_steps, n_features
     )
 
     X = X.transpose(1, 2)